llama3.1:8b

llama · 8.0B · Q4_K_M

Mac Mini (Apple M4 Pro)

64 GB · macOS 15.7.4

Tested on February 28, 2026

Top 67% Compare

Global Score

64 /100

Not Rec.

Hardware Fit

56/100

Quality

70/100

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Ollama

ollama pull llama3.1:8b

View on Ollama Library

ollama.com/library/llama3.1

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GGUF versions & quantizations

Hardware

Machine: Mac Mini
CPU: Apple M4 Pro
Cores: 14 total (10 perf + 4 eff)
Frequency: 2.4 GHz
RAM: 64 GB LPDDR5
GPU: Apple M4 Pro
OS: macOS 15.7.4
Arch: arm64

Performance

Tokens/sec Tokens generated per second — higher is better: 5.5
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±1.7
Time to first token How fast the model starts responding — lower is better: 1.4 s
Load time Time to load the model into memory before inference: 3.7 s
Memory usage RAM consumed during inference — percentage of total system memory: 28.6 GB (45%)
Total tokens Total number of tokens generated across all benchmark prompts: 997

Score breakdown

Speed

5/40

Time to first token

22/30

Memory

29/30

Quality

Reasoning

12/20

Coding

17/20

Instruction following

14/20

Structured output

13/15

Math

4/15

Multilingual

10/10

Category levels

Reasoning: Adequate Coding: Strong Instruction Following: Adequate Structured Output: Strong Math: Weak Multilingual: Strong

Metadata

Spec version: 0.1.0
Runtime: Ollama 0.17.4
Model format: GGUF
Hardware profile: HIGH-END
Result hash: 7981e64efe711bdaad8975e34885500b1fb4977fe6bf51f2591d61ddf9e9b0b9

Interpretation

Hardware fit: 56/100. Overall suitability: NOT RECOMMENDED (Global 64/100). Category profile: Reasoning: Adequate, Coding: Strong, Instruction Following: Adequate, Structured Output: Strong, Math: Weak, Multilingual: Strong.

Warnings

Token speed is unstable (stddev 1.7 tok/s, mean 5.5 tok/s) — may indicate thermal throttling or memory pressure.

Disqualifiers

Token speed too low: 5.5 tok/s (minimum: 6 tok/s for HIGH-END profile)

Run yours now

npx metrillm@latest bench

Requires Node 20+ and Ollama running